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Masked Image Definition: A masked image is an image in which some of the pixel values in the image are to be set to zero or other values as per the requirements. In simpler terms, it is an image editing process. For example, you have taken your selfie but don’t want the background to appear in yourRead more
Masked Image
Definition:
A masked image is an image in which some of the pixel values in the image are to be set to zero or other values as per the requirements.
In simpler terms, it is an image editing process. For example, you have taken your selfie but don’t want the background to appear in your photo for any reason. What you can do for his situation is that you can mask/alter the part of the image you don’t want.
Also, if you want some portion of the image to be brighter, you can do so without altering the whole background with this technique. Masking enables you to easily control the image layers’ specific parts with ease.
There are various software tools for image masking, and their process to mask the image might be different. However, the objective of each image masking software tool is the same.
Need of Masking
To makes changes later
To control transparencies of portion of the image
Removing or Replacing backgrounds of translucent objects
For making collage images
Creating different areas of the image visible
Masking Techniques
The first way to do masking is by using an image as a mask.
The other way is to use a set of ROI or Regions of Interest as masks.
Types of Image Masking
Layer Masking
Clipping Masking
Alpha Channel Masking
Masked Image Modeling
The masked Image Modeling technique has become popular because of its ability to learn from huge volumes of unlabeled data. This technique is quite effective for various natural image vision tasks.
Masked signal learning is a sort of machine learning in which the masked component of the input is applied to learn and predict the masked signal. This sort of learning may be found in NLP for self-supervised learning. Masked signal modelling is used in numerous studies to learn from large amounts of unannotated data.
When it comes to the computer vision challenge, this strategy may compete with other approaches such as contrastive learning. Masked image modelling is the process of doing computer vision tasks with masked pictures.
Framework of Masked Image Modelling
The objective of the masking procedures is to learn representation by enabling masked image modelling technique. The technique is able to mask a section of an image signal and anticipate the original signals at the masked region A motivational framework may include the following components: